{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Glass Quality Prediction  Weekend Hackathon 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We humans have been using glass since ancient times for a variety of applications from building construction to making decorative objects. With technology, glass and its applications have evolved, and today, we have different varieties of glass used for very different purposes from a computer monitor to a bulletproof car window depending on the grade of the glass produced. And not all grades or varieties are manufactured the same way. In this data science challenge, you as a data scientist must use the given data to predict the grade of the glass produced based on the given factors.\n",
    "\n",
    "**Given are 15 distinguishing factors that can provide insight into what grade of the glass is being produced. Your objective as a data scientist is to build a machine learning model that can predict the grade of glass based on the given factors.**\n",
    "\n",
    "**Data Description:-**\n",
    "The unzipped folder will have the following files.\n",
    "\n",
    "- Train.csv – 1358 observations.\n",
    "- Test.csv – 583 observations.\n",
    "- Sample Submission – Sample format for the submission.\n",
    "- Target Variable: class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "from sklearn.model_selection import train_test_split,cross_val_predict\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/Train.csv\")\n",
    "test = pd.read_csv(\"data/Test.csv\")\n",
    "sub = pd.read_excel(\"data/Sample_Submission.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grade_A_Component_1</th>\n",
       "      <th>grade_A_Component_2</th>\n",
       "      <th>max_luminosity</th>\n",
       "      <th>thickness</th>\n",
       "      <th>xmin</th>\n",
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       "      <th>x_component_3</th>\n",
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       "      <th>x_component_5</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>135</td>\n",
       "      <td>65</td>\n",
       "      <td>950</td>\n",
       "      <td>974</td>\n",
       "      <td>1038442</td>\n",
       "      <td>1036754</td>\n",
       "      <td>300</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>220</td>\n",
       "      <td>1705580</td>\n",
       "      <td>1705604</td>\n",
       "      <td>6803</td>\n",
       "      <td>6803.778622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>69</td>\n",
       "      <td>462</td>\n",
       "      <td>466</td>\n",
       "      <td>1088124</td>\n",
       "      <td>1086579</td>\n",
       "      <td>251</td>\n",
       "      <td>251.401943</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   grade_A_Component_1  grade_A_Component_2  max_luminosity  thickness  xmin  \\\n",
       "0                    0                    1             150         36   144   \n",
       "1                    1                    0             134         55  1144   \n",
       "2                    1                    0             135         65   950   \n",
       "3                    0                    1             123         35    41   \n",
       "4                    1                    0             138         69   462   \n",
       "\n",
       "   xmax     ymin     ymax  pixel_area     log_area  x_component_1  \\\n",
       "0   172   947225   947332         439   439.099273              0   \n",
       "1  1152  2379058  2379624         329   329.205616              1   \n",
       "2   974  1038442  1036754         300   300.120598              0   \n",
       "3   220  1705580  1705604        6803  6803.778622              0   \n",
       "4   466  1088124  1086579         251   251.401943              0   \n",
       "\n",
       "   x_component_2  x_component_3  x_component_4  x_component_5  class  \n",
       "0              0              1              0              0      1  \n",
       "1              0              0              0              0      1  \n",
       "2              0              0              0              0      2  \n",
       "3              0              1              0              0      1  \n",
       "4              0              0              0              0      2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>309634</td>\n",
       "      <td>310824</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>75</td>\n",
       "      <td>23</td>\n",
       "      <td>79</td>\n",
       "      <td>5368307</td>\n",
       "      <td>5367467</td>\n",
       "      <td>862</td>\n",
       "      <td>862.499179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   grade_A_Component_1  grade_A_Component_2  max_luminosity  thickness  xmin  \\\n",
       "0                    1                    0             123         65    33   \n",
       "1                    0                    1             158         99   125   \n",
       "2                    0                    1             120         35    64   \n",
       "3                    0                    1             130         39    57   \n",
       "4                    1                    0             142         75    23   \n",
       "\n",
       "   xmax      ymin      ymax  pixel_area     log_area  x_component_1  \\\n",
       "0    54   1646439   1646893         632   632.391749              0   \n",
       "1   132    189874    189529         421   421.928609              0   \n",
       "2    75  12986873  12986862         272   272.212214              1   \n",
       "3   177    309634    310824        3312  3312.310581              0   \n",
       "4    79   5368307   5367467         862   862.499179              0   \n",
       "\n",
       "   x_component_2  x_component_3  x_component_4  x_component_5  \n",
       "0              1              0              0              0  \n",
       "1              0              0              0              0  \n",
       "2              0              0              0              0  \n",
       "3              0              1              0              0  \n",
       "4              0              0              0              0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   grade_A_Component_1  grade_A_Component_2  x_component_1  x_component_2  \\\n",
       "0                    0                    1              0              0   \n",
       "1                    1                    0              1              0   \n",
       "2                    1                    0              0              0   \n",
       "3                    0                    1              0              0   \n",
       "4                    1                    0              0              0   \n",
       "\n",
       "   x_component_3  x_component_4  x_component_5  \n",
       "0              1              0              0  \n",
       "1              0              0              0  \n",
       "2              0              0              0  \n",
       "3              1              0              0  \n",
       "4              0              0              0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['grade_A_Component_1','grade_A_Component_2','x_component_1','x_component_2','x_component_3','x_component_4','x_component_5']].head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Grade_A feature and x_component are one hot encoded.\n",
    "- grade_A_Component_1 and grade_A_component_2 represent the same, there is a dummy variable.\n",
    "- So, **grade_A_Component_1** is removed from features.\n",
    "- In case of x_component, that is not the case all the 5 features are needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   pixel_area     log_area\n",
       "0         439   439.099273\n",
       "1         329   329.205616\n",
       "2         300   300.120598\n",
       "3        6803  6803.778622\n",
       "4         251   251.401943"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['pixel_area','log_area']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Since the interger part of pixel_area and log_area are same, we remove pixel_area or log_area by considering the feature importance and log_losss metric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are features which are created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(df):\n",
    "    \n",
    "    df['x'] = df['xmax']-df['xmin']\n",
    "    df['y'] = df['ymax']-df['ymin']\n",
    "    df['area'] = df['x']*abs(df['y'])\n",
    "    df['volume'] = df['area']*df['thickness']\n",
    "    df['diff_area'] = df['log_area'] - df['pixel_area']\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = create_features(train)\n",
    "test = create_features(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution plots for the numeric features\n",
    "fig, ax = plt.subplots(nrows=6, ncols=2,figsize=(12,12))\n",
    "\n",
    "ax = ax.flatten()\n",
    "i=0\n",
    "\n",
    "for feat in ['max_luminosity', 'thickness','xmin','xmax','ymin', 'ymax','pixel_area','log_area','x','y','area']:\n",
    "    sns.distplot(a=train[feat],bins=20,ax=ax[i])\n",
    "    i+=1\n",
    "    \n",
    "fig.delaxes(ax[11])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution plots for the numeric features\n",
    "fig, ax = plt.subplots(nrows=3, ncols=2,figsize=(12,12))\n",
    "\n",
    "ax = ax.flatten()\n",
    "i=0\n",
    "\n",
    "for feat in ['x_component_1','x_component_2','x_component_3','x_component_4','x_component_5']:\n",
    "    sns.countplot(x=feat,data=train,hue='class',ax=ax[i])\n",
    "    i+=1\n",
    "    \n",
    "fig.delaxes(ax[5])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** From these plots we can see that x_component with 1's are all classified as class 1.\n",
    "- This can be used directly for classification with probabilty as 1 for class 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing result \n",
    "def confident_results(test,sub):\n",
    "    for i in range(0,test.shape[0]):\n",
    "        if(test['x_component_1'][i]==1):\n",
    "            sub[1][i] =1.0\n",
    "            sub[2][i] =0.0\n",
    "        elif(test['x_component_2'][i]==1):\n",
    "            sub[1][i] =1.0\n",
    "            sub[2][i] =0.0\n",
    "        elif(test['x_component_3'][i]==1):\n",
    "            sub[1][i] =1.0\n",
    "            sub[2][i] =0.0\n",
    "        elif(test['x_component_4'][i]==1):\n",
    "            sub[1][i] =1.0\n",
    "            sub[2][i] =0.0\n",
    "        elif(test['x_component_5'][i]==1):\n",
    "            sub[1][i] =1.0\n",
    "            sub[2][i] =0.0\n",
    "    return sub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After trying models with different features these features provided lower log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x_component_2',\n",
       " 'max_luminosity',\n",
       " 'ymax',\n",
       " 'x_component_5',\n",
       " 'log_area',\n",
       " 'xmin',\n",
       " 'grade_A_Component_2',\n",
       " 'ymin',\n",
       " 'x_component_3',\n",
       " 'x_component_1',\n",
       " 'volume',\n",
       " 'x',\n",
       " 'area',\n",
       " 'x_component_4',\n",
       " 'xmax',\n",
       " 'thickness']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(set(train.columns)-set(['class','grade_A_Component_1','y','pixel_area','diff_area'])) \n",
    "target = 'class'\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric(y,y0):\n",
    "    return log_loss(y,y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_fold_cross_valid(model,train,features,target,n_splits=5):\n",
    "    \n",
    "    X = train[features].copy()\n",
    "    y = train[target].copy()\n",
    "\n",
    "    from sklearn.model_selection import KFold\n",
    "    kf = KFold(n_splits=5)\n",
    "    kf.get_n_splits(X)\n",
    "    res = []\n",
    "\n",
    "    for train_index, test_index in kf.split(X):\n",
    "        \n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "        model.fit(X_train,y_train)\n",
    "        y_pred = model.predict_proba(X_test)\n",
    "        res.append(metric(y_test,y_pred))\n",
    "        \n",
    "    print(\"LogLoss:\",np.array(res).mean())   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogLoss: 0.2845587601255075\n"
     ]
    }
   ],
   "source": [
    "model = ExtraTreesClassifier(random_state=11)\n",
    "k_fold_cross_valid(model,train,features,target,n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExtraTreesClassifier(random_state=11)\n",
    "model.fit(train[features],train[target])\n",
    "y_pred = model.predict_proba(test[features])\n",
    "sub = pd.DataFrame(y_pred,columns=[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = confident_results(test,sub)\n",
    "sub.to_excel(\"extratrees_addv1.xls\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>0.59</td>\n",
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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